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SASSnet

Code for paper: Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images(MICCAI 2020)

Our code is origin from UA-MT

You can find paper in Arxiv.

Usage

  1. Clone the repo:
git clone https://github.com/kleinzcy/SASSnet.git 
cd SASSnet

  1. Put the data in data/2018LA_Seg_Training Set.

  2. Train the model

cd code
# for 16 label
python train_gan_sdfloss.py --gpu 0 --label 16 --consistency 0.01 --exp model_name
# for 8 label
python train_gan_sdfloss.py --gpu 0 --label 8 --consistency 0.015 --exp model_name

Params are the best setting in our experiment.

  1. Test the model
python test_LA.py --model model_name --gpu 0 --iter 6000

Our best model are saved in model dir.

Citation

If you find our work is useful for you, please cite us.